Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bipartite Matching in Massive Graphs: A Tight Analysis of EDCS
Authors: Amir Azarmehr, Soheil Behnezhad, Mohammad Roghani
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement the factor-revealing LP in Section 3 for all possible values of β, β [1, 100] where β > β . The largest approximation ratio that we obtain using the factorrevealing LP is 0.6774 which is achieved for parameter β = 6 and β = 5 (see Table 1 and Figure 3). The experiments were conducted on a computing cluster equipped with 64 cores, each running at 2.30GHz on Intel(R) Xeon(R) processors, and with 756 Gi B of main memory. |
| Researcher Affiliation | Academia | 1Khoury College of Computer Science, Northeastern University, Boston, USA 2Department of Management Science and Engineering, Stanford University, Stanford, USA. |
| Pseudocode | Yes | LP 1: The factor-revealing LP for the approximation ratio of (β, β )-EDCS |
| Open Source Code | Yes | All of our code3 is written in Python (version 3.10.12) and is available in the supplementary material. ... 3The implemented code can be found at the following link. |
| Open Datasets | No | The paper analyzes a theoretical approximation ratio using a linear program and numerical solutions, rather than training a model on a traditional dataset. Therefore, there is no mention of a public or open dataset for training. |
| Dataset Splits | No | The paper does not involve traditional machine learning experiments with train/validation/test splits of data. It focuses on numerical solutions to a linear program. Therefore, no specific dataset split information for validation is provided. |
| Hardware Specification | Yes | The experiments were conducted on a computing cluster equipped with 64 cores, each running at 2.30GHz on Intel(R) Xeon(R) processors, and with 756 Gi B of main memory. |
| Software Dependencies | Yes | All of our code3 is written in Python (version 3.10.12)... For solving factorrevealing LP instances, we utilized the Gurobi optimization package (version 11.0.0). |
| Experiment Setup | Yes | We implement the factor-revealing LP in Section 3 for all possible values of β, β [1, 100] where β > β . The largest approximation ratio that we obtain using the factorrevealing LP is 0.6774 which is achieved for parameter β = 6 and β = 5. |